All the other great AIs

For all the enthusiasm around generative AI, it’s only the newest form of machine-learning technology. And while the ability to run queries in natural language has opened up AI to a wider audience and more applications, generative AI isn’t always the ideal tool for every enterprise application of AI. In this column from Hamidou Dia , we learn about the constellation of AI tools that are at the core of building AI agents capable of performing multiple tasks and greatly extending the reach of your AI technology.

The same way you’ve been hearing all about generative AI for the past year, you may have noticed that the next Next Big Thing has already entered the chat: AI agents .

As we’ve quickly discovered, generative AI is one of the most powerful technologies developed in generations. But there is also an entire constellation of established AI technologies — many of which are purpose-built to perform certain business tasks – and when combined with generative AI, can accomplish even more.

Much of the magic of gen AI that’s been capturing our attention is rarely achieved on its own. Some of the most exciting innovations we’ve come to associate with generative tools actually come from tapping into other AI systems that help enable advanced reasoning, intelligent decision-making, multi-step planning, and taking complex actions. This revelation is laying the foundations for building sophisticated, connected AI agents that can serve customers, empower employees, amplify creativity, and accelerate coding or data analytics.

Put simply: You don’t always need gen AI, but when you do, it can become even more powerful when used in concert with other types of AI.

As we enter the era of the AI agent, it’s worth remembering that gen AI is only one technology in a much larger AI toolbox that’s already currently available — and you’ll likely need to leverage every AI at your disposal to make the most of this transformative technological moment. That’s what AI leaders are already doing .

There’s more to AI than one technology

Foundation models and large language models, or LLMs, can create virtually any type of content, such as text, images, video, audio, or even computer code. The machine learning models that power generative AI capabilities can detect patterns and structures in their training data and create completely new content with similar characteristics.

The potential and general accessibility of this kind of AI is broadly what has excited so many business leaders. While other kinds of AI have been around for awhile, they often required specialized knowledge or expertise. The exciting confluence now is how gen AI is not only made more useful with input from other types of AI — gen AI interfaces can also make it easier to understand, operate, and manipulate more technical forms of AI, such as predictive modeling or vision and audio recognition.

Let’s say you ask an AI agent, “Which of these stocks outperformed their benchmarks in the last six months?” A gen AI model alone doesn't have the functional capabilities to look up and ask questions about different forecasts related to your business. In this case, gen AI is responsible for interpreting the prompt, triggering a function call to get the forecast, and then interpreting that forecast to provide an answer. But actually generating the forecast falls to a different type of AI system entirely, not the gen AI model running the rest of that experience.

Additionally, if you want your model to be smarter about interpreting your prompt asking about stock performance, grounding the model in relevant information — whether that be search data or your enterprise data — can greatly enhance the outputs. Also, by understanding the semantic meaning of a user's prompt, generative AI models can better grasp the desired outcome. These two steps - grounding and semantic search - help users generate results from generative AI more relevant to their business and their use case.

According to McKinsey research , generative AI could increase the impact of all AI technologies by as much as 40% — an estimate that roughly doubled when considering generative capabilities embedded into other tools used for tasks beyond established use cases.

Overall, such predictions underscore that as AI continues to mature, organizations will increasingly rely on a mix of several different technologies to create the AI agents that bring gen AI potential to life. And as organizations are eagerly exploring the future potential of AI, it’s imperative to start exploring all the AI technologies out there and start planning a clear path to AI agents.

With that in mind, let’s take a look at some of the most popular AI technologies that are becoming more accessible, extendable, and powerful with help from gen AI:

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